(1) Field of Invention
The present invention relates to searching for objects in visual imagery and, more specifically, to a method and system for a directed area search using cognitive swarm vision and cognitive Bayesian reasoning.
(2) Description of Related Art
Current systems that aim to automatically search for targets such as factories, airports, etc., in wide area overhead imagery face several major problems. These problems include finding targets hidden in terabytes of information, relatively few pixels on target, and long intervals between interesting regions. Furthermore, time-consuming analysis by humans requires many analysts. Also, in real life situations, no a priori representative examples or templates of interest may be available to aid the system in its search. Current systems also lack the ability to detect multiple classes of objects, and fail to provide the combination of very high detection rates (at least 95% detection rates) and very low false alarm rates (for example significantly less than 10 false alarms per square mile).
Preliminary attempts to solve the aforementioned problems have concentrated on the simple aspects of combining domain knowledge with visual recognition. Liao (see literature reference no. 6 in the “List of Cited References” below). Such systems used domain knowledge and abstracted canonical models elicited from typical visual scenes for comparison. These systems are also highly tuned to specific object types and do not specify how context can be used to recognize multiple entities in the scene.
With regard to a top-down engine for guiding the search process, Liao (see literature reference no. 6) proposes an influence diagram as an active fusion model for representing, integrating, and inference of uncertain sensory information of different modalities at multiple levels of abstraction. However, the proposed influence diagram lacks flexibility and the ability to learn over time via operator feedback.
With regard to visual recognition, there have been several attempts at recognizing buildings, vehicles, etc, from aerial imagery that use high-level descriptions of the entities being searched. Jaynes (see literature reference No. 7) and Huertas (see literature reference No. 8). While operable for detection of a single class, these systems cannot effectively process multiple classes of objects under continuously changing knowledge context conditions.
Thus, a continuing need exists for a comprehensive system for directed area search which can process operator feedback to improve its domain knowledge over time and effectively process multiple classes of objects under continuously changing knowledge context conditions.